COMPARATIVE ANALYSIS OF TECHNICAL EFFICIENCY IN RICE PRODUCTION UNDER SMALL-SCALE FARMER MANAGED IRRIGATION SYSTEM AND RAIN-FED SYSTEM IN KOGI STATE

ABSTRACT

This study compared the technical efficiency of rice production under small-scale Farmer Managed Irrigation Systems (FMIS) and Rain-fed Systems (RFS) in Kogi State, Nigeria. The specific objectives were to: describe the socioeconomic characteristics of rice farmers in both systems; compare the input intensity and levels used in FMIS and RFS; determine the technical efficiency levels of farmers in both systems; identify the factors influencing technical efficiency; and compare the returns to scale in both production systems. The study adopted a survey research design with a multi-stage purposive sampling technique to compose a sample of 120 rice farmers (60 from FMIS and 60 from RFS) from Ibaji, Bassa, and Kogi Local Government Areas. Data were collected using structured questionnaires and analyzed using descriptive statistics, Levene’s test, robust tests for equality of means, Chow-break point test, and maximum likelihood estimation of stochastic frontier and inefficiency models. The findings revealed that the mean age of farmers was 42 years, with 72% being males, and an average farming experience of 16 years. FMIS farmers showed significantly higher input usage across land, fertilizer, labour, pesticides, and water. The mean technical efficiency for FMIS was 73%, while RFS farmers achieved 90%. Significantdifferences existed in technical efficiencies between the two groups, with both systems exhibiting decreasing returns to scale (0.813 for FMIS and 0.476 for RFS). Farming experience, education, and extension contacts significantlyinfluenced technical efficiency in FMIS, while education, extension contact, and age were significant in RFS. The study recommends capacity building for farmers and extension agents, public investment in irrigation projects, and public-private partnerships for resource conservation and input supply.

CHAPTER ONE

INTRODUCTION

1.1 Background of the Study

Rice (Oryza sativa and Oryza glaberrima) is a fundamental staple food crop in Nigeria, occupying a central position in the nation’s food security agenda and agricultural development strategy. As the most consumed cereal grain in the country, rice is integral to the daily diet of millions of Nigerians across all socioeconomic strata. Nigeria is the largest rice producer in West Africa and ranks among the top producers on the African continent. However, despite this significant production capacity, the country continues to grapple with a substantial supply-demand gap, with estimated annual demand of approximately 5 million metric tons against domestic production of about 2.21 million metric tons of milled product. This shortfall, estimated at 2.79 million metric tons annually, is bridged through importation at a cost of approximately $267 million, placing considerable strain on foreign exchange reserves and jeopardizing national food security objectives .

The productivity of rice in Nigeria has been constrained by various factors, including the prevalence of traditional production systems, limited adoption of improved technologies, inadequate access to productive resources, and environmentalchallenges. Average yields of upland and lowland rainfed rice in Nigeria hover around 1.8 tons per hectare, significantly below the achievable potential of over 4 tons per hectare achievable under optimal management conditions . This yield gap has been attributed to inefficiencies in resource utilization by smallholder farmers, who constitute the vast majority of rice producers in the country. Addressing these inefficiencies through appropriate interventions is critical for enhancing domestic rice production and reducing dependence on imports.

The production of rice in Nigeria is characterized by three major production systems: upland rainfed, lowland rainfed (including Fadama areas), and irrigated systems. Rain-fed systems, which account for the bulk of rice production, are highly dependent on rainfall patterns and are therefore vulnerable to the vagaries of climate variability and change. Irrigated systems, while offering the potential for more stable and higher yields, cover only a small proportion of the total rice area and are often constrained by infrastructural deficits and management challenges. Understanding the technical efficiency differences between these production systems is essential for designing interventions that can enhance productivity and close the rice supply-demand gap .

Kogi State, located in the North-Central geopolitical zone of Nigeria, is endowed with extensive lowland areas suitable for rice cultivation and has emerged as an important rice-producing state. The state’s agricultural landscape includes both rain-fed lowlands and areas where small-scale farmer-managed irrigation systems have been developed, particularly along the banks of the River Niger and its tributaries. The presence of both production systems in the state provides a unique opportunity for comparative analysis of their technical efficiency and the factors influencing productivity. The state’s rice production potential has been recognized by various agricultural development programmes, including the Fadama Development Project, which has supported rice farmers through the provision of improved inputs, infrastructure, and advisory services .

Farmer Managed Irrigation Systems (FMIS) represent an important category of irrigation where farmers themselves take responsibility for the operation and maintenance of irrigation infrastructure. These systems, often developed through community initiative or supported by government and development partners, enable farmers to cultivate rice during both wet and dry seasons, thereby increasing cropping intensity and annual production. In Kogi State, FMIS have been established in various communities, allowing farmers to access water from rivers, streams, and constructed dams for rice production. The management of these systems is typically organized through water user associations or farmer cooperatives, which coordinate water distribution, maintenanceactivities, and conflict resolution .

Rain-fed rice production, on the other hand, relies entirely on natural rainfall for crop water requirements. In Kogi State, rain-fed rice is predominantly cultivated in lowland areas (Fadama) where the water table is high enough to support rice growth during the rainy season. Rain-fed rice farmers typically plant rice at the onset of the rains and harvest at the end of the rainy season. While this system has lower capital requirements and operational costs compared to irrigation, it is subject to the risks of rainfall variability, drought, and flooding. The reliance on seasonal rainfall also means that farmers can only produce one crop per year, limiting their potential income and contribution to national rice supply .

Technical efficiency, defined as the ability of a farmer to obtain maximum output from a given set of inputs given the available technology, is a crucial concept in understanding agricultural productivity. Technical efficiency is measured relative to a production frontier, with efficient farmers operating on the frontier and inefficient farmers operating below it. Understanding the levels of technical efficiency and the factors that influence it is essential for designing interventions that can enhance productivity and improve the welfare of farmers. The stochastic frontier production function is a widely used econometric tool for estimating technical efficiency, as it allows for the separation of inefficiency effects from random noise .

The empirical investigation of technical efficiency in rice production has been the subject of numerous studies in Nigeria and other developing countries. These studies have generally found that technical efficiency levels vary considerably among farmers and across production systems, influenced by factors such as education, farming experience, access to extension services, credit availability, and farm size. In the context of Kogi State, Onoja and Achike  conducted a comprehensive study comparing the technical efficiency of rice farmers under FMIS and RFS. Their findings revealed that while FMIS farmers used higher input intensities across land, fertilizer, labour, pesticides, and water, the technical efficiency of FMIS farmers (73%) was lower than that of RFS farmers (90%). This counter-intuitive finding suggests that the additional inputs used in FMIS are not being translated into proportionally higher output, indicating significant inefficiency in input use.

The socioeconomic characteristics of rice farmers play a significant role in determining their technical efficiency. In Kogi State, studies have shown that the mean age of farmers is approximately 42 years, with about 72% being males. The average farming experience is around 16 years, and farmers have typically spent about 8 years in formal education . These characteristics influence farmers’ access to information, adoption of improved technologies, and decision-making capabilities, which in turn affect their technical efficiency. Understanding the socioeconomic profile of farmers in both FMIS and RFS is essential for identifying the factors that differentiate the two groups and for targeting interventions to specific categories of farmers.

The factors that influence technical efficiency differ between FMIS and RFS. In FMIS, farming experience, years of formal education, and frequency of extension contacts have been found to exert statistically significant positive effects on technical efficiency . This suggests that farmers with more experience, higher education, and better access to extension services are better able to use inputs efficiently and achieve higher output levels. In RFS, education, extension contact, and age of farmers have been identified as significant determinants of technical efficiency . The importance of extension contacts in both systems underscores the critical role of agricultural extension services in enhancing farmer knowledge and skills, promoting the adoption of improved technologies, and improving resource use efficiency.

The concept of returns to scale, which measures the responsiveness of output to a proportional change in all inputs, is another important aspect of production efficiency. Studies in Kogi State have estimated the returns to scale for FMIS and RFS at 0.813 and 0.476 respectively, indicating decreasing returns to scale in both systems . This means that increasing all inputs by a given percentage results in a less than proportional increase in output. For FMIS, the returns to scale are closer to constant returns (1.0), suggesting that farmers have more capacity to increase output through input expansion. For RFS, the low returns to scale (0.476) suggest that other constraints, such as water availability or management practices, are limiting the response to input increases.

The yield gap analysis between irrigated and rain-fed rice production systems in neighboring Kwara State provides further insights into the productivity differences between the two systems. Studies have shown that rice production is more profitable and efficient under irrigated systems than under rain-fed systems . The significant determinants of yield gap in both systems have been found to vary, with farm size and rice variety being common significant determinants in both systems. The comparative advantage of irrigated systems is reflected in higher yields, with irrigated rice achieving about 3.5 tons per hectare compared to 2.2 tons per hectare for rainfed lowland rice .

The role of extension services in improving technical efficiency cannot be overstated. Extension agents serve as the primary link between research institutions and farmers, disseminating information on improved technologies, production practices, and market opportunities. In Kogi State, the frequency of extension contacts has been identified as a significant determinant of technical efficiency for both FMIS and RFS farmers . This underscores the need for strengthening extension services, increasing the frequency of farmer-extension contacts, and improving the quality of extension advice. The training of extension agents in modern rice production technologies and participatory approaches can enhance their effectiveness in supporting farmers.

The Fadama Development Project, which has been implemented in Kogi State, provides an important context for understanding rice production and technical efficiency. The project, supported by the World Bank, employs a Community Driven Development approach to empower communities and associations to develop social and inclusive local development plans. The project has provided support to rice farmers through the provision of improved inputs, infrastructure (including irrigation structures), and advisory services. The impact of the project on technical efficiency has been mixed, with some studies showing that beneficiaries were more efficient than non-beneficiaries, while others found no significant difference between the two groups .

The constraints affecting rice production in Kogi State are numerous and interrelated, affecting both FMIS and RFS farmers. The most prevalent constraints include inadequate training on resource usage, pest and disease problems, inadequate storage facilities, limited access to credit, poor infrastructure, and high input costs . These constraints limit farmers’ ability to adopt improved technologies, apply inputs efficiently, and achieve optimal productivity. Addressing these constraints through targeted interventions is essential for enhancing technical efficiency and improving the welfare of rice farmers.

The theoretical framework for analyzing technical efficiency is rooted in production economics, specifically the concept of the production function. The stochastic frontier production function, developed by Aigner, Lovell, and Schmidt (1977) and Meeusen and van den Broeck (1977), provides a robust framework for estimating technical efficiency. The model separates the error term into a random component (capturing stochastic shocks and measurement errors) and an inefficiency component (capturing technical inefficiency). The maximum likelihood estimation method is used to estimate the parameters of the production function and the efficiency of individual farmers .

In conclusion, the background of this study establishes the context for the comparative analysis of technical efficiency in rice production under small-scale farmer managed irrigation system and rain-fed system in Kogi State. The study recognizes the importance of rice for food security and the economy, the role of irrigation in enhancing rice production, and the need for empirical evidence on technical efficiency to inform policy and practice. The following sections of this chapter present the statement of the problem, aim and objectives of the study, research questions, hypotheses, significance, scope, limitations, and definition of terms.

1.2 Statement of the Problem

Rice production in Nigeria is characterized by a significant supply-demand gap that has persisted despite various policy interventions and agricultural development programmes. The national rice supply-demand gap of approximately 2.79 million tons annually is bridged through importation at considerable foreign exchange cost, jeopardizing national food security objectives. Central to this challenge is the issue of technical efficiency among rice farmers, as average yields in Nigeria remain far below achievable potential, indicating significant inefficiencies in the use of productive resources.

Kogi State, as an important rice-producing state, has benefited from various agricultural development programmes, including the establishment and support of Farmer Managed Irrigation Systems. However, the technical efficiency of rice farmers in the state, particularly in comparison between FMIS and RFS, has not been adequately documented. There is a need to determine whether the higher input intensity associated with FMIS translates into higher technical efficiency, or whether inefficiencies in input use limit the productivity gains from irrigation.

The socioeconomic characteristics of rice farmers in Kogi State, including age, gender, education, farming experience, household size, and access to extension services, influence their technical efficiency. However, the comparative profile of FMIS and RFS farmers and the extent to which their socioeconomic characteristics differ have not been systematically analyzed. Understanding these characteristics is essential for identifying the factors that distinguish the two groups and for designing targeted interventions to enhance efficiency.

The input intensity and levels used in rice production differ between FMIS and RFS, reflecting differences in resource access, production practices, and management capacity. FMIS farmers typically use higher quantities of land, fertilizer, labour, pesticides, and water. However, the extent of these differences and their implications for technical efficiency have not been fully explored. Understanding the relationship between input intensity and technical efficiency is essential for identifying the optimal levels of input use and for advising farmers on efficient resource allocation.

The factors that influence technical efficiency in rice production in Kogi State, such as farming experience, education, extension contacts, and age, have been identified in previous studies. However, the comparative effects of these factors on FMIS and RFS farmers have not been adequately investigated. Understanding how these factors differ in their influence between the two systems is essential for targeting interventions to address specific constraints in each system.

The returns to scale in rice production in Kogi State have been estimated at decreasing levels for both FMIS and RFS, indicating that further input expansion may not lead to proportional increases in output. However, the implications of these returns to scale for policy and practice have not been fully explored. Understanding the scale economies in rice production is essential for advising farmers on optimal farm size and for designing interventions that address the structural constraints limiting productivity.

The constraints affecting rice production in Kogi State, including inadequate training, pest and disease problems, inadequate storage facilities, limited access to credit, and poor infrastructure, have been identified but their relative importance for FMIS and RFS farmers has not been compared. Understanding the specific constraints facing each group is essential for prioritizing interventions and allocating resources effectively.

The Fadama Development Project and other agricultural interventions have provided support to rice farmers in Kogi State, including the establishment of irrigation infrastructure and the provision of improved inputs and advisory services. However, the impact of these interventions on technical efficiency has not been adequately evaluated. There is a need to assess whether project beneficiaries are more technically efficient than non-beneficiaries and to identify the factors that contribute to or limit efficiency gains from project participation.

The yield gap between potential and actual rice yields in Kogi State reflects the technical inefficiency of rice farmers and the constraints they face. However, the determinants of the yield gap in FMIS and RFS have not been adequately investigated. Understanding the factors that contribute to the yield gap in each system is essential for designing interventions that can close the gap and enhance productivity.

The role of extension services in improving technical efficiency has been recognized, but the effectiveness of extension services in Kogi State, particularly in supporting FMIS and RFS farmers, has not been adequately evaluated. There is a need to assess the frequency and quality of extension contacts, the relevance of extension advice, and the impact of extension on technical efficiency.

In light of these challenges, there is an urgent need to conduct a comparative analysis of technical efficiency in rice production under small-scale farmer managed irrigation system and rain-fed system in Kogi State. This study aims to address the knowledge gaps identified above and provide evidence-based recommendations for enhancing the efficiency, productivity, and profitability of rice farming in the state. The findings are expected to contribute to policy development, program design, and institutional strengthening for agricultural development in Kogi State and beyond.

1.3 Aim of the Study

The aim of this study is to conduct a comparative analysis of technical efficiency in rice production under small-scale farmer managed irrigation system and rain-fed system in Kogi State, Nigeria.

1.4 Objectives of the Study

Specifically, the study seeks to:

1. Describe the socioeconomic characteristics of rice farmers under Farmer Managed Irrigation Systems (FMIS) and Rain-fed Systems (RFS) in Kogi State.
2. Compare the input intensity and levels used in rice production under FMIS and RFS.
3. Determine the technical efficiency levels of rice farmers under FMIS and RFS.
4. Identify the factors influencing technical efficiency in rice production under both systems.
5. Compare the returns to scale in rice production under FMIS and RFS.

1.5 Research Questions

The following research questions guide this study:

1. What are the socioeconomic characteristics of rice farmers under Farmer Managed Irrigation Systems and Rain-fed Systems in Kogi State?
2. How does input intensity and levels used in rice production compare between FMIS and RFS?
3. What are the technical efficiency levels of rice farmers under FMIS and RFS?
4. What factors influence technical efficiency in rice production under both systems?
5. How do returns to scale compare between rice production under FMIS and RFS?

1.6 Research Hypotheses

The following null (H₀) and alternative (H₁) hypotheses have been formulated to guide this study and will be tested at 0.05 level of significance:

Hypothesis 1

• H₀: There is no significant difference in the socioeconomic characteristics of rice farmers under Farmer Managed Irrigation Systems and Rain-fed Systems in Kogi State.
• H₁: There is a significant difference in the socioeconomic characteristics of rice farmers under Farmer Managed Irrigation Systems and Rain-fed Systems in Kogi State.

Hypothesis 2

• H₀: There is no significant difference in the input intensity and levels used in rice production under FMIS and RFS in Kogi State.
• H₁: There is a significant difference in the input intensity and levels used in rice production under FMIS and RFS in Kogi State.

Hypothesis 3

• H₀: There is no significant difference in the technical efficiency levels of rice farmers under FMIS and RFS in Kogi State.
• H₁: There is a significant difference in the technical efficiency levels of rice farmers under FMIS and RFS in Kogi State.

Hypothesis 4

• H₀: Socioeconomic factors do not significantly influence technical efficiency in rice production under FMIS and RFS in Kogi State.
• H₁: Socioeconomic factors significantly influence technical efficiency in rice production under FMIS and RFS in Kogi State.

Hypothesis 5

• H₀: There is no significant difference in the returns to scale in rice production under FMIS and RFS in Kogi State.
• H₁: There is a significant difference in the returns to scale in rice production under FMIS and RFS in Kogi State.

1.7 Significance of the Study

The findings of this study will be significant in several ways to various stakeholders in agricultural development, policy formulation, and rice production in Kogi State and Nigeria.

To the academic community, this study will contribute to the growing body of knowledge on technical efficiency in agricultural production in Nigeria, specifically in the area of rice production. The findings will serve as a reference material for researchers, students, and scholars interested in agricultural efficiency, production economics, and programme evaluation. The study will also identify gaps in current knowledge that can be addressed through further research, thereby stimulating additional studies on technical efficiency and agricultural development.

For policymakers at the federal, state, and local government levels, the findings of this study will provide evidence-based information to inform the development of policies and programs that support rice production and agricultural development. The study will identify priority areas for intervention, including irrigation development, extension services, input supply, and credit provision. Policy recommendations from this study can contribute to the implementation of the Agricultural Promotion Policy and other agricultural development strategies.

Government agencies responsible for agricultural development, including the Kogi State Ministry of Agriculture, the Kogi State Agricultural Development Programme (KSADP), and the National Agricultural Extension and Research Liaison Services (NAERLS), will benefit from the study’s findings by gaining a better understanding of the technical efficiency of rice farmers and the constraints they face. The study will identify areas where interventions are needed and suggest strategies for enhancing efficiency and productivity.

The Fadama Coordination Office and other project implementation units will benefit from the study by understanding the effectiveness of their interventions in improving technical efficiency. The study will provide evidence on the outcomes of the Fadama project and other agricultural programmes, identifying lessons for future project design and implementation.

Rice farmers, including both FMIS and RFS farmers, will benefit from the study through increased awareness of technical efficiency and strategies for improving their productivity and profitability. The study will identify factors that facilitate or constrain efficiency and provide recommendations for optimizing resource use. By involving farmers in the research process, the study will also empower them to participate in policy discussions and advocate for their interests.

Agricultural extension agents and development practitioners working with rice farmers in Kogi State will benefit from the study’s findings by gaining a better understanding of the constraints facing farmers and the strategies for improving efficiency. The study will identify training needs and suggest approaches for integrating efficiency improvement into extension services. These findings can inform the design and delivery of extension programs that support rice farmers.

The banking and financial sector will benefit from the study by understanding the investment needs and constraints of rice farmers. The study will provide insights into the profitability of rice farming and the factors that influence efficiency, informing financial institutions’ lending practices and product development for the agricultural sector.

The study will also contribute to the broader goal of food security and poverty reduction by highlighting the importance of technical efficiency for national rice self-sufficiency. By demonstrating the differences in efficiency between FMIS and RFS, the study will make the case for investment in irrigation development and other interventions that can enhance productivity.

In terms of methodology, the study will contribute to the development of research approaches for analyzing technical efficiency and comparing production systems. The use of stochastic frontier production function and related analytical techniques will serve as a model for efficiency analysis in agricultural research.

Finally, the study will contribute to public awareness and understanding of rice production and the importance of technical efficiency for agricultural development. By disseminating the findings through publications, seminars, and community outreach, the study will educate stakeholders about the challenges and opportunities in rice production and the need for policies and interventions that support efficient resource use.

1.8 Scope of the Study

This study is delimited to the comparative analysis of technical efficiency in rice production under small-scale farmer managed irrigation system and rain-fed system in Kogi State, Nigeria.

Geographically, the study covers Kogi State, which is located in the North-Central geopolitical zone of Nigeria. The state has significant rice production areas and hosts both FMIS and RFS. The study covers three Agricultural Zones where rice is produced in commercial quantities, specifically Ibaji, Bassa, and Kogi Local Government Areas. These LGAs were selected based on the availability of commercial rice farms and the presence of both FMIS and RFS.

Content-wise, the study focuses on five key dimensions of technical efficiency: socioeconomic characteristics, input intensity and levels, technical efficiency levels, factors influencing technical efficiency, and returns to scale. These dimensions were selected based on their significance for understanding efficiency and productivity in rice production and their implications for policy and practice.

The study focuses on rice production, which is the main crop cultivated in both FMIS and RFS in Kogi State. The study does not address other crops or agricultural enterprises. The study covers both wet and dry season production where applicable, reflecting the seasonal variations in production practices and efficiency.

The study population comprises rice farmers in Kogi State, including both FMIS and RFS farmers. The study includes 120 respondents (60 from each system) selected through a multi-stage purposive sampling technique. The study does not include other stakeholders such as project staff, extension agents, or input suppliers, although their perspectives could provide additional insights.

Temporally, the study focuses on the period when the data were collected, capturing the production practices and efficiency of rice farmers. Primary data were collected during the farming season, providing a snapshot of current conditions and relationships.

Methodologically, the study adopts a survey research design using structured questionnaires for primary data collection. Data analysis employs descriptive statistics, Levene’s test, robust tests for equality of means, Chow-break point test, and maximum likelihood estimation of stochastic frontier and inefficiency models. These analytical techniques are well established in efficiency analysis and enable comprehensive comparison of the two production systems.

1.9 Limitation of the Study

This study acknowledges several limitations that may affect the interpretation and generalizability of its findings.

The use of a survey design with self-report questionnaires is one of the major limitations of this study. This design relies on respondents’ ability to accurately recall and report their production practices, input use, and output levels. Recall bias, where respondents may not remember details of their farming activities accurately, could affect the validity of the findings. Additionally, respondents may have provided socially desirable responses, particularly on sensitive topics such as income and resource use, limiting the accuracy of the data.

The sample size of 120 respondents (60 FMIS and 60 RFS), while adequate for the statistical analysis planned, may limit the statistical power of the study and the generalizability of the findings. The sample may not adequately capture the diversity of rice production practices and efficiency levels across the different Local Government Areas of Kogi State. A larger sample size would have enhanced the representativeness of the findings and enabled more detailed subgroup analyses.

The study was conducted in Kogi State only and may not be representative of other states in Nigeria or other regions of the country. The specific production conditions, efficiency levels, and irrigation systems in Kogi State may differ from those in other areas, limiting the generalizability of the findings. However, the study provides insights that may be relevant to similar agricultural contexts and can inform policy and practice beyond the immediate study area.

The study focused on rice farmers as the primary respondents and did not include project staff, extension agents, or input suppliers, whose perspectives could have enriched the study. Project staff could provide insights into irrigation system design and management, while extension agents could provide information on advisory services and technology transfer. The exclusion of these groups may have introduced a bias towards the perspectives of farmers.

The cross-sectional design of the study, which collects data at a single point in time, does not allow for the assessment of changes over time or the establishment of causal relationships between production system characteristics and technical efficiency. The study is limited to comparing FMIS and RFS farmers at a single point in time and cannot determine whether observed differences are due to the production system or to pre-existing differences between the groups.

The reliance on a structured questionnaire, while allowing for standardized data collection and quantitative analysis, may not have captured the full complexity of farmers’ production practices, decision-making processes, and efficiency challenges. The pre-defined response options may not have adequately reflected the range of practices, constraints, and opportunities in the study area. Qualitative data collection methods, such as focus group discussions and in-depth interviews, could have provided richer insights.

Selection bias may affect the comparison between FMIS and RFS farmers, as farmers who participate in FMIS may differ systematically from those who do not. Farmers who are more motivated, better connected, or have more resources may be more likely to participate in irrigation schemes, leading to differences that are not attributable to the production system itself. The study does not employ econometric techniques to control for selection bias, which may affect the validity of the comparison.

Financial constraints limited the scope of the study, including the geographical coverage, sample size, and data collection methods. With more resources, the study could have included a larger sample, additional data collection methods, and a broader range of stakeholders. The financial limitations also affected the ability to conduct pilot studies and test the research instrument extensively before the main data collection.

Time constraints were another limitation, as the study had to be completed within a specific academic timetable. This limited the ability to conduct extensive community mobilization, engage in prolonged fieldwork, and verify responses through repeated visits or triangulation with other data sources. The study also could not include longitudinal data collection to capture seasonal and interannual variability in efficiency and production.

Language and communication challenges may have affected the quality of data collected, particularly if research assistants did not have adequate proficiency in the local languages spoken by respondents. Translating complex concepts such as technical efficiency, stochastic frontier, and returns to scale into local languages may have led to some loss of meaning or misunderstanding. The use of trained research assistants and careful translation of the questionnaire mitigated this limitation.

Despite these limitations, the study provides valuable insights into technical efficiency in rice production under FMIS and RFS in Kogi State and offers a foundation for further research and policy development. The limitations identified do not invalidate the findings but should be taken into account when interpreting and applying the results.

1.10 Definition of Terms

The following terms are defined as they are used in the context of this study:

Technical Efficiency: Refers to the ability of a farmer to obtain maximum output from a given set of inputs, given the available technology. Technical efficiency is measured relative to a production frontier, with efficient farmers operating on the frontier and inefficient farmers operating below it. It is expressed as a ratio between actual and potential output.

Farmer Managed Irrigation System (FMIS): Refers to an irrigation system where farmers themselves take responsibility for the operation and maintenance of irrigation infrastructure. These systems are often developed through community initiative or supported by government and development partners, enabling farmers to cultivate rice during both wet and dry seasons.

Rain-fed System (RFS): Refers to a rice production system that relies entirely on natural rainfall for crop water requirements. Rain-fed rice is predominantly cultivated in lowland areas (Fadama) where the water table is high enough to support rice growth during the rainy season.

Stochastic Frontier Production Function: Refers to an econometric model used to estimate technical efficiency by separating the error term into a random component (capturing stochastic shocks and measurement errors) and an inefficiency component (capturing technical inefficiency). The model is estimated using maximum likelihood methods.

Returns to Scale: Refers to the responsiveness of output to a proportional change in all inputs. Increasing returns to scale occur when a proportional increase in all inputs leads to a more than proportional increase in output. Decreasing returns to scale occur when the increase in output is less than proportional.

Elasticity of Output: Refers to the percentage change in output resulting from a one percent change in a particular input, holding other inputs constant. Elasticities are estimated from the production function and indicate the responsiveness of output to changes in input levels.

Input Intensity: Refers to the quantity of inputs used per unit of land or per unit of output. Higher input intensity indicates greater use of inputs such as fertilizer, labour, pesticides, and water in the production process.

Inefficiency Model: Refers to a model that identifies the factors influencing technical inefficiency, such as socioeconomic characteristics of farmers. The inefficiency model is estimated simultaneously with the stochastic frontier production function.

Maximum Likelihood Estimation (MLE): Refers to a statistical method for estimating the parameters of a model by maximizing the likelihood function. MLE is used to estimate the parameters of the stochastic frontier production function and the inefficiency model.

Fadama: Refers to the low-lying floodplains and wetlands in Nigeria that are suitable for dry season agriculture, particularly rice production. Fadama areas provide opportunities for irrigation and year-round cultivation.

Water Users Association (WUA): Refers to a community-based organization formed by water users to coordinate water distribution, maintenance activities, and conflict resolution in irrigation systems. WUAs are important for the effective management of FMIS.

Extension Contact: Refers to the interaction between farmers and agricultural extension agents, through which farmers receive information, advice, and training on improved agricultural practices. Frequency of extension contact is a measure of farmers’ access to extension services.

Yield Gap: Refers to the difference between potential or attainable yield and actual yield achieved by farmers. The yield gap reflects the technical inefficiency of farmers and the constraints they face in production.